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We have written a couple of times in the past about Microservices. The approaches are evolving, and this blog is an attempt to address a specific question -while testing microservices, does test automation have a role?

Just a little refresher first. As the name suggests, microservices are nothing but a combination of multiple small services that make up a whole. It is a unique method of developing software systems that focus on creating single-function modules with well-defined interfaces and operations. An application built as microservices can be broken down into multiple component services. Each of these services can be deployed, modified, and then redeployed individually without compromising the integrity of an application. This enables you to change one or more distinct services (as and when required) instead of having to redeploy the application as a whole.

Microservices are also highly intelligent. They receive requests, process them, and produce a response accordingly. They have smart points that process information and apply logic, and then direct the flow of the information.

Microservices architecture is ideal in the case of evolutionary systems, for eg. where it is not possible to thoroughly anticipate the types of devices that may be accessing the application in the future. Many software products start based on a monolithic architecture but can be gradually revamped to microservices as and when unforeseen requirements surface that interact over an older unified architecture through APIs.

Why is Testing for Microservices Complicated?

In the traditional approach to testing, every bit of code needs to be tested individually using unit tests. As parts are consolidated together, they should be tested with integration testing. Once all these tests pass, a release candidate is created. This, in turn, is put through system testing, regression testing, and user-acceptance testing. If all is well, QA will sign-off, and the release will roll out. This might be accelerated while developing in Agile, but the underlying principle would hold.

This approach does not work for testing microservices. This is mainly because apps built on microservices use multiple services. All these services may not be available on staging at the same time or in the same form as they are during production. Secondly, microservices scale up and share the demand. Therefore, testing microservices using traditional approaches can be difficult. In that scenario, an effective way to conduct microservices testing is to leverage test automation.

Quick Tips on How to Automate Testing for Microservices:

Here are some quick tips that will help you while testing your microservices-based application using test automation.

Manage each service as a software module.

List the essential links in your architecture and test them

Do not attempt to gather the entire microservices environment in a small test setup.

Test across different setups.

How to Conduct Test Automation for Microservices?

Each Service Should Be Tested Individually:Test automation can be a powerful mechanism for testing microservices. It is relatively easy to create a simple test script that regularly calls the service and matches a known set of inputs against a proposed output. This function by itself will free up your testing team’s time and allow them to concentrate on testing that is more complex.

Test the Different Functionalities of your Microservices-based Application: Once the vital functional elements of the microservices-based application have been identified, they should be tested much like you would conduct integration testing in the traditional approach. In this case, the benefits of test automation are obvious. You can quickly generate test scripts that are run each time one of the microservices is updated. By analyzing and comparing the outputs of the new code with the previous one, you can establish if anything has changed or has broken.

Refrain from Testing in a Small Setup:Instead of conducted testing in small local environments, consider leveraging cloud-based testing. This allows you to dynamically allocate resources as your tests need them and freeing them up when your tests have completed.

Test Across Diverse Setups: While testing microservices, use multiple environments to test your code. The reason behind this is to expose your code to even slight variations in parameters like underlying hardware, library versions, etc. that might affect it when you deploy to production.

Microservices architecture is a powerful idea that offers several benefits for designing and implementing enterprise applications. This is why it is being adopted by several leading software development organizations. A few examples of inspirational software teams leveraging microservices include Netflix, Amazon, eBay, etc. If like these software teams, your product development is also adopting microservices then testing would undoubtedly be in focus. As we have seen, testing these applications is a complex task and traditional methods will not do the job. To thoroughly test an application built on this model, it may be essential to adopt test automation. Would you agree?

According to Gartner, by 2020, AI technologies will be pervasive in almost every new product and service and will also be a top investment priority for CIO’s. 2018 really was all about Artificial Intelligence. Tech giants such as Microsoft, Facebook, Google, Amazon and the like spent billions on their AI initiatives. We started noticing the rise of AI as an enterprise technology. It’s now clear how AI brings new intelligence to everything it touches by exploiting the vast sea of data at hand. Influential voices also started talking about the paradigm shift that this technology would bring to the world of software development. Of course, software testing too has not remained immune to the charms of AI.

Role: AI In Software Testing.

But first, Why do we Need AI for Software Testing?

It seems like we have only just firmly established the role of test automation in the software testing landscape and we must start preparing for further disruptions promised by AI! The rise of test automation was driven by development methodologies such as Agile and the need to ship bug and error-free, robust software products into the market faster. From there we have progressed into the era of daily deployments with the rise of DevOps. DevOps is pushing organizations to accelerate the QA cycle even further, to reduce test overheads, and to enable superior governance. Automating test requirement traceability and versioning are also factors that now need careful consideration in this new development environment.

The “surface area” of testing has also increased considerably. As applications interact with one another through API’s leveraging legacy systems, the complexity tends to increase as the code suites keep growing. As the software economy grows and enterprises push towards digital transformation, businesses now demand real-time risk assessment across the different stages of the software delivery cycle.

The use of AI in software testing could emerge as a response to these changing times and environments. AI could help in developing failsafe applications and to enable greater automation in testing to meet these expanded expectations from testing.

How will AI work in Software Testing?

As we move deeper into the age of digital disruption, the traditional ways of developing and delivering software are inadequate to fuel innovation. Delivery timelines are reducing but the technical complexity is rising. With Continuous Testing gradually becoming the norm, organizations are trying to further accelerate the testing process to bridge the chasm between development, testing, and operations in the DevOps environment.

AI helps organizations achieve this pace of accelerated testing and helps them test smarter and not harder. AI has been called, “A field of study that gives computers the ability to learn without being explicitly programmed”. This being the case, organizations can leverage AI to drive automaton by leveraging both supervised and unsupervised methods.

An AI-powered testing platform can easily recognize changed controls promptly. The constant updates in the algorithms will ensure that even the slightest changes can be identified easily.

AI in test automation can be employed for object application categorizations for all user interfaces very effectively. Upon observing the hierarchy of controls, testers can create AI enabled technical maps that look at the graphical user interface (GUI) and easily obtain the labels for different controls.

AI can also be employed effectively to conduct exploratory testing within the testing suite. Risk preferences can be assigned, monitored, and categorized easily with AI. It can help testers in creating the right heat maps to identify bottlenecks in processes and help in increasing test accuracy.

AI can be leveraged effectively to identify behavioral patterns in application testing, defect analysis, non-functional analytics, analysis data from social media, estimation, and efficiency analysis. Machine Learning, a part of AI, algorithms can be employed to test programs and to generate robust test data and deep insights, making the testing process more in-depth and accurate.

AI can also increase the overall test coverage and the depth and the scope of the tests as well. AI algorithms in software testing can be put to work for test suite optimization, enhancing UI testing, traceability, defect analysis, predicting the next test for queuing, determine pass/fail outcomes for complex and subjective tests, rapid impact analysis etc. Since 80% of all tests are repetitive, AI can free up the tester’s time and helps them focus on the more creative side of testing.

Conclusion:

Perhaps the ultimate objective of using AI in software testing is to aim for a world where the software will be able to test, diagnose, and self-correct. This could enable quality engineering and could further reduce the testing time from days to mere hours. There are signs that the use of AI in software testing can save time, money, and resources and help the testers focus their attention on doing the one thing that matters – release great software.

This is now a software-defined world. Almost every company today is a technology company. Every product, in some way, is a technology product. As businesses lean more heavily on technology and software, the software development and technology landscape become even more dynamic. Technology is in a constant state of flux, with one shiny new object outshining the one from yesterday. The stakeholders of software development, the testers, developers, designers etc. thus need to constantly re-evaluate their skills. In this environment of constant change, here are, in my opinion, the five most in-demand technology skills to possess today, and why?

R:Owing to the advances in machine learning, the R programming language is having its coming of age moment now. This open source language has been a workhorse for sorting and manipulating large data sets and has shown its versatility in model building, statistical operations, and visualizations. R, over the years, has become a foundational tool in expanding AI to unlock large data blocks. As data became more dominant, R has made itself quite comfortable in the data science arena. In fact, this language is predicted to surpass the use of Python in data science as R, in contrast to Python, allows robust statistical models to be written in just a few lines. As the world falls more in love with data science it will also find itself getting closer to R.

React: Amongst client-side technologies, React has been growing in popularity rapidly. While the number of frameworks based on JavaScript continues to increase, React still dominates this space. Open Sourced by Facebook in 2013, React has been climbing up the technology charts owing to its ease of use, high level of flexibility and responsiveness, its virtual DOM (document object model) capabilities, its downward data binding capabilities, the ease of enabling migrations, and light-weightiness. React is also winning in the NPM download race and has won the crown of the Best JavaScript framework of 2018. In the age of automation, React gives developers a framework that allows them to break down complex components and reuse codes to complete projects faster. Its unique syntax that allows HTML quotes, as well as HTML tag syntax, help in promoting construction of machine-readable codes. React also gives developers the flexibility to break down complex UI/UX development into simpler components and allows them to make every component intuitive. It also has excellent runtime performance.

Swift: In 2017 we heard reports of the declining popularity of Swift. One of the main reasons for the same was a perceived preference among developers’ to use multiplatform tools. Swift, that is merely four years old, ranked 16 on the TIOBE index despite having a good start. The reason was mainly the changing methodologies in the mobile development ecosystem. However, in 2018 we seem to be witnessing the rise of Swift once again. According to a study conducted by analyst firm RedMonk, Swift tied with Object C at rank 10 in their January 2018 report. It fell one place in the June report, but that could be attributed to the lack of a server-side presence, something IBM has been working to rectify in keeping with its enterprise push. Once Swift became open source it has grown in popularity and has also matured as a language. With iOS apps proving to be more profitable than Android apps, we can expect more developers to switch to Swift. Swift is also finding its way into business discussions as enterprises look at robust iOS apps that offer performance as well as security.

Test Automation:Organizations are racing to achieve business agility. This drive has promoted the rise of new development methodologies and the move towards continuous integration and continuous delivery. In this need for speed Test automation will continue to rise in prominence as it enables faster feedback. The push towards digital transformation in enterprises is also putting the focus on testing and quality assurance. I expect Shift-left testing to grow to hasten software development. Test automation is rapidly emerging as the enabler of software confidence. With the rising interest in new technologies like IoT and blockchain, test automation is expected to get a further push. The possible role of AI in testing is also something to look out for as AI could bring in more intelligence, validation, efficiency, and automation to testing. These could be exciting times for those in the testing and test automation space.

UX:Statistics reveal that 90% of users stop using an application with a bad UX. 86% of users uninstall an app if they encounter problems with its functionality in design. UX or User Experience will continue to rise in prominence as it is the UX that earns users interest and ultimately their loyalty. The business value of UX will rise even further as we delve deeper into the app economy. The role of UX designers is becoming even more compelling as we witness the rise of AR, chatbots and virtual assistants. With the software products and services market becoming increasingly competitive, businesses have to focus heavily on UX design to deliver intuitive and coherent experiences to their users that drive usage and foster adoption.

It is an exciting time for us in the technology game. Innovation, flexibility, simplicity, reliability, and speed have become important contributors to software success. The key differentiator in these dynamic times may be the technology skills that you as an individual or as a technology-focused organization possess. To my mind, the skills that will help you stay ahead are those I’ve identified here.

Let’s dive into Top 90 QA Interview Questions answers that we will recommend you while appearing for any QA interview.

What is Software Quality Assurance (SQA)?

Software quality assurance is an umbrella term, consisting of various planned process and activities to monitor and control the standard of whole software development process so as to ensure quality attribute in the final software product.

What is Software Quality Control (SQC)?

With the purpose similar to software quality assurance, software quality control focuses on the software instead to its development process to achieve and maintain the quality aspect in the software product.

What is Software Testing?

Software testing may be seen as a sub-category of software quality control, which is used to remove defects and flaws present in the software, and subsequently improves and enhances the product quality.

No, but the end purpose of all is same i.e. ensuring and maintaining the software quality.

Then, what’s the difference between SQA, SQC and Testing?

SQA is a broader term encompassing both SQC and testing in it and ensures software development process quality and standard and subsequently in the final product also, whereas testing which is used to identify and detect software defects is a sub-set of SQC.

Software testing life cycle defines and describes the multiple phases which are executed in a sequential order to carry out the testing of a software product. The phases of STLC are requirement, planning, analysis, design, implementation, execution, conclusion and closure.

How STLC is related to or different from SDLC (software development life cycle)?

Both SDLC and STLC depict the phases to be carried out in a subsequent manner, but for different purpose. SDLC defines each and every phase of software development including testing, whereas STLC outlines the phases to be executed during a testing process. It may be inferred that STLC is incorporated in the SDLC phase of testing.

Entry and exit criteria is defined and specified to initiate and terminate a particular testing process or activity respectively, when certain conditions, factors and requirements is/are being met or fulfilled.

What do you mean by the requirement study and analysis?

Requirement study and analysis is the process of studying and analysing the testable requirements and specifications through the combined efforts of QA team, business analyst, client and stakeholders.

What are the different types of requirements required in software testing?

SRS layouts the functional and non-functional requirements for the software to be developed whereas BRS reflects the business requirement i.e., the business demand of a software product as stated by the client.

Why there is a bug/defect in software?

A bug or a defect in software occurs due to various reasons and conditions such as misunderstanding or requirements, time restriction, lack of experience, faulty third party tools, dynamic or last time changes, etc.

What is a software testing artifact?

Software testing artifact or testing artifact are the documents or tangible products generated throughout the testing process for the purpose of testing or correspondence amongst the team and with the client.

What are test plan, test suite and test case?

Test plan defines the comprehensive approach to perform testing of the system and not for the single testing process or activity. A test case is based on the specified requirements & specifications define the sequence of activities to verify and validate one or more than one functionality of the system. Test suite is a collection of similar types of test cases.

How to design test cases?

Broadly, there are three different approaches or techniques to design test cases. These are

Black box design technique, based on requirements and specifications.

White box design technique based on internal structure of the software application.

Experience based design technique based on the experience gained by a tester.

What is test environment?

A test environment comprises of necessary software and hardware along with the network configuration and settings to simulate intended environment for the execution of tests on the software.

Why test environment is needed?

Dynamic testing of the software requires specific and controlled environment comprising of hardware, software and multiple factors under which a software is intended to perform its functioning. Thus, test environment provides the platform to test the functionalities of software in the specified environment and conditions.

What is test execution?

Test execution is one of the phases of testing life cycle which concerns with the execution of test cases or test plans on the software product to ensure its quality with respect to specified requirements and specifications.

What are the different levels of testing?

Generally, there are four levels of testing viz. unit testing, integration testing, system testing and acceptance testing.

What is unit testing?

Unit testing involves the testing of each smallest testable unit of the system, independently.

What is the role of developer in unit testing?

As developers are well versed with their lines of code, they are preferred and being assigned the responsibility of writing and executing the unit tests.

What is integration testing?

Integration testing is a testing technique to ensure proper interfacing and interaction among the integrated modules or units after the integration process.

What are stubs and drivers and how these are different to each other?

Stubs and drivers are the replicas of modules which are either not available or have not been created yet and thus they works as the substitutes in the process of integration testing with the difference that stubs are used in top bottom approach and drivers are used in bottom up approach.

What is system testing?

System testing is used to test the completely integrated system as a one system against the specified requirements and specifications.

What is acceptance testing?

Acceptance testing is used to ensure the readiness of a software product with respect to specified requirement and specification in order to get readily accepted by the targeted users.

Different types of acceptance testing.

Broadly, acceptance testing is of two types-alpha testing and beta testing. Further, acceptance testing can also be classified into following forms:

Both alpha and beta testing are the forms of acceptance testing where former is carried out at development site by the QA/testing team and the latter one is executed at client site by the intended users.

What are the different approaches to perform software testing?

Generally, there are two approaches to perform software testing viz. Manual testing and Automation. Manual testing involves the execution of test cases on the software manually by the tester whereas automation process involves the usage of automation framework and tools to automate the task of test scripts execution.

What is the advantage of automation over manual testing approach and vice-versa?

Is there any testing technique that does not needs any sort of requirements or planning?

Yes, but with the help of test strategy using check lists, user scenarios and matrices.

Difference between ad-hoc testing and exploratory testing?

Both ad-hoc testing and exploratory testing are the informal ways of testing the system without having proper planning & strategy. However, in ad-hoc testing, a tester is well-versed with the software and its features and thereby carries out the testing whereas in exploratory, he/she gets to learn and explore more about the software during the course of testing and thus tests the system gradually along with software understanding and learning throughout the testing process.

How monkey testing is different from ad-hoc testing?

Both monkey and ad-hoc testing are the informal approach of testing but in monkey testing, a tester does not requires the pre-understanding and detailing of the software, but learns about the product during the course of testing whereas in ad-hoc testing, tester has the knowledge and understanding of the software.

Why non-functional testing is equally important to functional testing?

Functional testing tests the system’s functionalities and features as specified prior to software development process. It only validates the intended functioning of the software against the specified requirement and specification but the performance of the system to function in the unexpected circumstances and conditions in real world environment at the users end and to meet customer satisfaction is done through non-functional testing technique. Thus, non-functional testing looks after the non-functional traits of the software.

Which is a better testing methodology: black-box testing or white-box testing?

Both black-box and white-box testing approach have their own advantages and disadvantages. Black-box testing approach enables testers to externally test the system on the basis of specified requirement and specification and does not provide the scope of testing the internal structure of the system, whereas white-box testing methodology verify and validates the software quality through testing of its internal structure and working.

If black-box and white-box, then why gray box testing?

Gray box testing is a third type of testing and a hybrid form of black-box and white-box testing approach, which provides the scope of externally testing the system using test plans and test cases derived from the knowledge and understanding of internal structure of the system.

Difference between static and dynamic testing of software.

The primary difference between static and dynamic testing approach is that the former does not involves the execution of code to test the system whereas latter approach requires the code execution to verify and validate the system quality.

Smoke and Sanity testing are used to test software builds. Are they similar??

Although, both smoke and sanity testing is used to test software builds but smoke testing is used to test the initial build which are unstable whereas sanity tests are executed on relatively stable builds which had undergone multiple time through regression testing.

When, what and why to automate?

Automation is preferred when the execution of tests needs to be carried out repetitively for a longer period of time and within the specified deadlines. Further, an analysis of ROI on automation is desired to analyse the cost-benefit model of the automation. Preferably functional, regression and functional tests may be automated. Further, tests which requires accuracy and precision, and is time-consuming may be considered for automation, including data driven tests also.

What are the challenges faced in automation?

Some of the common challenges faced in the automation are

Initial cost is very high along with the maintenance costs. Thus, requires proper analysis to assess ROI on automation.

Increased complexities.

Limited time.

Demands skilled tester, having appropriate knowledge of programming.

Automation training cost and time.

Selection of right and appropriate tools and frameworks.

Less flexible.

Keeping test plans and cases updated and maintained.

Difference between retesting and regression testing.

Both retesting and regression testing is done after modification in software features and configuration to remove or correct the defect(s). However, retesting is done to validate that the identified defects has been removed or resolved after applying patches while regression testing is done to ensure that the modification in the software doesn’t impacts or affects the existing functionalities and originality of the software.

How to categorize bugs or defects found in the software?

A bug or a defect may be categorized on the priority and severity basis, where priority defines the need to correct or remove defect, from business perspective, whereas severity states the need to resolve or eliminate defect from software requirement and quality perspective.

What is the importance of test data?

Test data is used to drive the testing process, where diverse types of test data as inputs are provided to the system to test the response, behaviour and output of the system, which may be desirable or unexpected.

Why agile testing approach is preferred over traditional way of testing?

Agile testing follows the agile model of development, which requires no or less documentation and provides the scope of considering and implementing the dynamic and changing requirements along with the direct involvement of client or customer to work on their regular feedbacks and requirements to provide software in multiple and short iterative cycles.

What are the parameters to evaluate and assess the performance of the software?

Parameters which are used to evaluate and assess the performance of the software are active defects, authored tests, automated tests, requirement coverage, no. of defects fixed/day, tests passed, rejected defects, severe defects, reviewed requirements, test executed and many more.

How important is the localization and globalization testing of a software application?

Globalization and localization testing ensures the software product features and standards to be globally accepted by the world wide users and to meet the need and requirements of the users belonging to a particular culture, area, region, country or locale, respectively.

Verification is done throughout the development phase on the software under development whereas validation is performed over final product produced after the development process with respect to specified requirement and specification.

Does test strategy and test plan define the same purpose?

Yes, the end purpose of test strategy and test plan is same i.e. to works as a guide or manual to carry out the software testing process, but still they both differs.

Which is better approach to perform regression testing: manual or automation?

Automation would provide better advantage in comparison to manual for performing regression testing.

What is bug life cycle?

Bug or Defect life cycle describes the whole journey or the life of a defect through various stages or phases, right from when it is identified and till its closure.

No, as one of the principles of software testing states that exhaustive testing is not possible.

Why exploratory testing is preferred and used in the agile methodology?

As agile methodology requires the speedy execution of the processes through small iterative cycles, thereby calls for the quick, and exploratory testing which does not depends on the documentation work and is carried out by tester through gradual understanding of the software, suits best for the agile environment.

Difference between load and stress testing.

The primary purpose of load and stress testing is to test system’s performance, behaviour and response under different varied load. However, stress testing is an extreme or brutal form of load testing where a system under increasing load is subjected to certain unfavourable conditions like cut down in resources, short or limited time period for execution of task and various such things.

What is data driven testing?

As the name specifies, data driven testing is a type of testing, especially used in the automation, where testing is carried out and drive by the defined sets of inputs and their corresponding expected output.

When to start and stop testing?

Basically, on the availability of software build, testing process starts. However, testing may be started early with the development process, as soon as the requirements are gathered and available. Moreover, testing depends upon the requirement of the software development model like in waterfall model, testing is done in the testing phase, whereas in agile testing is carried out in multiple and short iteration cycle.

Testing is an infinite process as it is impossible to make a software 100% bug free. But still, there are certain conditions specified to stop testing such as:

The primary advantage of using the traceability matrix is that it maps the all the specified requirements with that to test cases, thereby ensures complete test coverage.

What is software testability?

Software testability comprises of various artifacts which gives the estimation about the efforts and time required in the execution of a particular testing activity or process.

What is positive and negative testing?

Positive testing is the activity to test the intended and correct functioning of the system on being fed with valid and appropriate input data whereas negative testing evaluates the system’s behaviour and response in the presence of invalid input data.

Cookie is used to store the personal data and information of a user at server location, which is later used for making connections to web pages by the browsers, and thus it is essential to test these cookies.

A QA engineer has multiple roles and is bounded to several responsibilities such as defining quality parameters, describing test strategy, executing test, leading the team, reporting the defects or test results.

What is rapid software testing?

Rapid software testing is a unique approach of testing which strikes out the need of any sort of documentation work, and motivates testers to make use of their thinking ability and vision to carry out and drive the testing process.

Difference between error, defect and failure.

In the software engineering, error defines the mistake done by the programmers. Defect reflects the introduction of bugs at production site and results into deviation in results from its expected output due to programming mistakes. Failure shows the system’s inability to execute functionalities due to presence of defect. i.e. defect explored by the user.

Whether security testing and penetration testing are similar terms?

No, but both testing types ensure the security mechanism of the software. However, penetration testing is a form of security testing which is done with the purpose to attack the system to ensure not only the security features but also its defensive mechanism.

Distinguish between priority and severity.

Priority defines the business need to fix or remove identified defect whereas severity is used to describe the impact of a defect on the functioning of a system.

What is test harness?

Test harness is a term used to collectively define various inputs and resources required in executing the tests, especially the automated tests to monitor and assess the behaviour and output of the system under different varied conditions and factors. Thus, test harness may include test data, software, hardware and many such things.

What constitutes a test report?

A test report may comprise of following elements:

Objective/purpose

Test summary

Logged defects

Exit criteria

Conclusion

Resources used

What are the test closure activities?

Test closure activities are carried out the after the successful delivery or release of the software product. This includes collection of various data, information, testwares pertaining to software testing phase so as to determine and assess the impact of testing on the product.

List out various methodologies or techniques used under static testing.

System testing is done with the perspective to test the system against the specified requirements and specification whereas acceptance testing ensures the readiness of the system to meet the needs and expectations of a user.

Distinguish between use case and test case.

Both use case and test case is used in the software testing. Use case depicts and defines the user scenarios including various possible path taken by the system under different conditions and circumstances to execute a particular task and functionality. On the other side, test case is a document based on the software and business requirements and specification to verify and validate the software functioning.

What is the need of content testing?

In the present era, content plays a major role in creating and maintaining the interest of the users. Further, the quality content attracts the audience, makes them convinced or motivated over certain things, and thus is a productive input for the marketing purpose. Thus, content testing is a must testing to make your software content suitable for your targeted users.

List out different types of documentation/documents used in the software testing.

Test plan.

Test scenario.

Test cases.

Traceability Matrix.

Test Log and Report.

What is test deliverables?

Test deliverables are the end products of a complete software testing process- prior, during and after the testing, which is used to impart testing analysis, details and outcomes to the client.

What is fuzz testing?

Fuzz testing is used to discover coding flaws and security loopholes by subjecting system with the large amount of random data with the intent to break the system.

How testing is different with respect to debugging?

Testing is done with the purpose of identifying and locating the defects by the testing team whereas debugging is done by the developers to fix or correct the defects.

What is the importance of database testing?

Database is an inherited component of a software application as it works as a backend system of the application and stores different types of data and information from multiple sources. Thus, it is crucial to test the database to ensure integrity, validity, accuracy and security of the stored data.

What are the different types of test coverage techniques?

Statement Coverage

Branch Coverage

Decision Coverage

Path Coverage

Why and how to prioritize test cases?

Due to abundance of test cases for the execution within the given testing deadline arises the need to prioritize test cases. Test prioritization involves the reduction in the number of test cases, and selecting & prioritizing only those which are based on some specific criteria.

How to write a test case?

Test cases should be effective enough to cover each and every feature and quality aspect of software and able to provide complete test coverage with respect to specified requirements and specifications.

How to measure the software quality?

There are certain specified parameters, namely software quality metrics which is used to assess the software quality. These are product metrics, process metrics and project metrics.

What are the different types of software quality model?

Mc Call’s Model

Boehm Model

FURPS Model

IEEE Model

SATC’s Model

Ghezzi Model

Capability Maturity Model

Dromey’s quality Model

ISO-9126-1 quality model

What different types of testing may be considered and used for testing the web applications?

Functionality testing

Compatibility testing

Usability testing

Database testing

Performance testing

Accessibility testing

What is pair testing?

Pair testing is a type of ad-hoc testing where pair of testers or tester and developer or tester & user is being formed which are responsible for carrying out the testing of the same software product on the same machine.

Hope these 90 QA Questions has provided you a complete overview of the QA process. We wish above QA interview questions will help you clear your next QA interview. Do share your feedback with us @ [email protected] and let us know how these QA questions have helped you during your QA interview.

Testing your newly-designed code for bugs and malfunction is an important part of the development process. After all, your application or piece of code will be used in different systems, environments, and scenarios after shipping.

According to statistics, 36% of developers claim that they will not implement any new coding techniques or technologies in their work at least for the coming year. This goes to show how fast the turnaround times are in the software development world.

It’s often better to ship a slightly less ambitious but functional product than a groundbreaking, unstable one. However, you can achieve both if you automate your quality assurance processes carefully. Let’s take a look at how and why you should automate your functional tests for a quick and valuable feedback during the coding process.

Benefits of Functional Testing & Automation:

Maintaining your Reputation: Whether you are a part of a large software development company or an independent startup project, your reputation plays a huge role in the public perception of your work. Research shows that 17% of developers agree that unrealistic expectations are the biggest problem in their respective fields. Others state that lack of goal clarity, prioritization, and a lack of estimation also add to the matter. There is always a dissonance between managers and developers, which leads to crunch periods and very quick product delivery despite a lack of QA testing. Automated functional testing of your code can help you maintain a professional image by shipping a working product at the end of the development cycle.

Controlled Testing Environment: One of the best parts of in-house testing is the ability to go above and beyond with how much stress you put on your code. For example, you can strain the application or API with as much incoming data and connections as possible without the fear of the server crashing or some other anomaly. While you can never predict how your code will be used in practice, you can assume as many scenarios as possible and test for those specific scenarios.

Early Bug Detection: Most importantly, functional test automation allows for constant, day-to-day testing of your developed code. You can detect bugs, glitches, and data bottlenecks very quickly in doing so. That way, you will detect problems early in the development stage without relying on test group QA which will or will not come across practical issues. The bugs you discover early on can sometimes steer your development process in an entirely different direction, one that you would be oblivious to without automated, repeated testing.

Is Your Test’s Automation Necessary? Before you decide to design your automated functionality test, it’s important to gauge its necessity in the overall scheme of things. Do you really need an automated test at this moment or can you test your code’s functionality manually for the time being? The reason behind this question is simple – the use of too much automated testing can have adverse effects on the data you collect from it. More importantly, test design takes time and careful scripting, both of which are valuable in the project’s development process. Make sure that you are absolutely sure that you need automated tests at this very moment before you step into the scripting process.

Separate Testing from Checking: Testing and checking are two different things, both of which correlate with what we said previously. In short, when you “check” your code, you will be fully aware, engaged, and present for the process. Testing, on the other hand, is automated and you will only see the end-results as the final data rolls in. Both testing and checking are important in the QA of your project, but they can in no way replace one another. Make sure that both are implemented in equal measure and that you double-check everything that seems off or too good to be true manually.

Map out the Script Fully: Running a partial script through your code won’t bring any tangible results to the table. Worse yet, it will confuse your developers and lead to even more crunch time. Instead, make sure that your script is fully written and mapped out before you put it into automated testing. Make sure that the functional test covers each aspect of your code instead of opting for selective testing. This will ensure that the code is tested for any conflicts and compatibility issues instead of running a step-by-step test.

Multiple Tests with Slight Variations: What you can do instead of opting for several smaller tests is to introduce variations into your functionality test script. Include several variations in terms of scenarios and triggers which your code will go through in each testing phase. This will help you determine which aspects of your project need more polish and which ones are good as they are. Repeated tests with very small variations in between are a great way to vent out any dormant or latent bugs which can rear their head later on. Avoid unnecessary post-launch bug fixes and last-minute changes by introducing a multi-version functionality test early on.

Go for Fast Turnaround: While it is important to check off every aspect of your code in the functional testing phase, it is also important to do so in a timely manner. Don’t rely on overly-complex or long tests in your development process. Even with automation and high-quality data to work with afterward, you will still be left with a lot of analysis and rework to be done as a result. Design your scripts so that they trigger every important element in your code without going into full top-to-bottom testing each time you do so. That way, you will have a fast and reliable QA system available for everyday coding – think of it as your go-to spellcheck option as you write your essay.

Identify & Patch Bottlenecks: Lastly, it’s important to patch out the bottlenecks, bugs, and glitches you receive via the functional test you automated. Once these problems are ironed out, make sure to run your scripts again and check if you were right in your assertion. Running the script repeatedly without any fixes in between runs won’t yield any productive data. As a result, the entire process of functional test automation falls flat due to its inability to course-correct your development autonomously.

In Summation

Once you learn what mistakes are bound to happen again and again, you will also learn to fix them preemptively by yourself without the automated testing script. Use the automation feature as a helpful tool, not as a means to fix your code (which it won’t do by itself).

Patch out your glitches before moving forward and closer to the official launch or delivery of your code to the client. The higher the quality of work you deliver, the better you will be perceived as a professional development firm. It’s also worth noting that you will learn a lot as a coder and developer with each bug that comes your way.

Author: Elisa Abbott is a freelancer whose passion lies in creative writing. She completed a degree in Computer Science and writes about ways to apply machine learning to deal with complex issues. Insights on education, helpful tools, and valuable university experiences – she has got you covered;) When she’s not engaged in assessing translation services for PickWriters you’ll usually find her sipping a cappuccino with a book.

Nowadays, quality is the driving force behind the popularity as well as the success of a software product, which has drastically increased the requirement to take effective measures for quality assurance. Therefore, to ensure this, software testers are using a defined way of measuring their goals and efficiency, which has been made possible with the use of various software testing metrics and key performance indicators(KPI’s). The metrics and KPIs serve a crucial role and help the team determine the metrics that calculate the effectiveness of the testing teams and help them gauge the quality, efficiency, progress, and the health of the software testing.

Therefore, to help you measure your testing efforts and the testing process, our team of experts have created a list of some critical software testing metrics as well as key performance indicators based on their experience and knowledge.

The Fundamental Software Testing Metrics:

Software testing metrics, which are also known as software test measurement, indicates the extent, amount, dimension, capacity, as well as the rise of various attributes of a software process and tries to improve its effectiveness and efficiency imminently. Software testing metrics are the best way of measuring and monitoring the various testing activities performed by the team of testers during the software testing life cycle. Moreover, it helps convey the result of a prediction related to a combination of data. Hence, the various software testing metrics used by software engineers around the world are:

Derivative Metrics: Derivative metrics help identify the various areas that have issues in the software testing process and allows the team to take effective steps that increase the accuracy of testing.

Defect Density: Another important software testing metrics, defect density helps the team in determining the total number of defects found in a software during a specific period of time- operation or development. The results are then divided by the size of that particular module, which allows the team to decide whether the software is ready for the release or whether it requires more testing. The defect density of a software is counted per thousand lines of the code, which is also known as KLOC. The formula used for this is:

Defect Density = Defect Count/Size of the Release/Module

Defect Leakage: An important metric that needs to be measured by the team of testers is defect leakage. Defect leakage is used by software testers to review the efficiency of the testing process before the product’s user acceptance testing (UAT). If any defects are left undetected by the team and are found by the user, it is known as defect leakage or bug leakage.

Defect Leakage = (Total Number of Defects Found in UAT/ Total Number of Defects Found Before UAT) x 100

Defect Removal Efficiency: Defect removal efficiency (DRE) provides a measure of the development team’s ability to remove various defects from the software, prior to its release or implementation. Calculated during and across test phases, DRE is measured per test type and indicates the efficiency of the numerous defect removal methods adopted by the test team. Also, it is an indirect measurement of the quality as well as the performance of the software. Therefore, the formula for calculating Defect Removal Efficiency is:

DRE = Number of defects resolved by the development team/ (Total number of defects at the moment of measurement)

Defect Category: This is a crucial type of metric evaluated during the process of the software development life cycle (SDLC). Defect category metric offers an insight into the different quality attributes of the software, such as its usability, performance, functionality, stability, reliability, and more. In short, the defect category is an attribute of the defects in relation to the quality attributes of the software product and is measured with the assistance of the following formula:

Defect Category = Defects belonging to a particular category/ Total number of defects.

Defect Severity Index: It is the degree of impact a defect has on the development of an operation or a component of a software application being tested. Defect severity index (DSI) offers an insight into the quality of the product under test and helps gauge the quality of the test team’s efforts. Additionally, with the assistance of this metric, the team can evaluate the degree of negative impact on the quality as well as the performance of the software. Following formula is used to measure the defect severity index.

Review Efficiency: The review efficiency is a metric used to reduce the pre-delivery defects in the software. Review defects can be found in documents as well as in documents. By implementing this metric, one reduces the cost as well as efforts utilized in the process of rectifying or resolving errors. Moreover, it helps to decrease the probability of defect leakage in subsequent stages of testing and validates the test case effectiveness. The formula for calculating review efficiency is:

Review Efficiency (RE) = Total number of review defects / (Total number of review defects + Total number of testing defects) x 100

Test Case Effectiveness: The objective of this metric is to know the efficiency of test cases that are executed by the team of testers during every testing phase. It helps in determining the quality of the test cases.

Test Case Productivity: This metric is used to measure and calculate the number of test cases prepared by the team of testers and the efforts invested by them in the process. It is used to determine the test case design productivity and is used as an input for future measurement and estimation. This is usually measured with the assistance of the following formula:

Test Coverage: Test coverage is another important metric that defines the extent to which the software product’s complete functionality is covered. It indicates the completion of testing activities and can be used as criteria for concluding testing. It can be measured by implementing the following formula:

Test Coverage = Number of detected faults/number of predicted defects.

Another important formula that is used while calculating this metric is:Requirement Coverage = (Number of requirements covered / Total number of requirements) x 100

Test Design Coverage: Similar to test coverage, test design coverage measures the percentage of test cases coverage against the number of requirements. This metric helps evaluate the functional coverage of test case designed and improves the test coverage. This is mainly calculated by the team during the stage of test design and is measured in percentage. The formula used for test design coverage is:

Test Design Coverage = (Total number of requirements mapped to test cases / Total number of requirements) x 100

Test Execution Coverage: It helps us get an idea about the total number of test cases executed as well as the number of test cases left pending. This metric determines the coverage of testing and is measured during test execution, with the assistance of the following formula:

Test Execution Coverage = (Total number of executed test cases or scripts / Total number of test cases or scripts planned to be executed) x 100

Test Tracking & Efficiency: Test efficiency is an important component that needs to be evaluated thoroughly. It is a quality attribute of the testing team that is measured to ensure all testing activities are carried out in an efficient manner. The various metrics that assist in test tracking and efficiency are as follows:

Fixed Defects Percentage: With the assistance of this metric, the team is able to identify the percentage of defects fixed.

(Defect fixed / Total number of defects reported) x 100

Accepted Defects Percentage: The focus here is to define the total number of defects accepted by the development team. These are also measured in percentage.

(Defects accepted as valid / Total defect reported) x 100

Defects Rejected Percentage: Another important metric considered under test track and efficiency is the percentage of defects rejected by the development team.

(Number of defects rejected by the development team / total defects reported) x 100

Defects Deferred Percentage: It determines the percentage of defects deferred by the team for future releases.

(Defects deferred for future releases / Total defects reported) x 100

Critical Defects Percentage: Measures the percentage of critical defects in the software.

(Critical defects / Total defects reported) x 100

Average Time Taken to Rectify Defects: With the assistance of this formula, the team members are able to determine the average time taken by the development and testing team to rectify the defects.

(Total time taken for bug fixes / Number of bugs)

Test Effort Percentage: An important testing metric, test efforts percentage offer an evaluation of what was estimated before the commencement of the testing process vs the actual efforts invested by the team of testers. It helps in understanding any variances in the testing and is extremely helpful in estimating similar projects in the future. Similar to test efficiency, test efforts are also evaluated with the assistance of various metrics:

Number of Test Run Per Time Period: Here, the team measures the number of tests executed in a particular time frame.(Number of test run / Total time)

Test Design Efficiency: The objective of this metric is to evaluate the design efficiency of the executed test.(Number of test run / Total Time)

Bug Find Rate: One of the most important metrics used during the test effort percentage is bug find rate. It measures the number of defects/bugs found by the team during the process of testing.(Total number of defects / Total number of test hours)Number of Bugs Per Test: As suggested by the name, the focus here is to measure the number of defects found during every testing stage.(Total number of defects / Total number of tests)

Average Time to Test a Bug Fix: After evaluating the above metrics, the team finally identifies the time taken to test a bug fix.(Total time between defect fix & retest for all defects / Total number of defects)

Test Effectiveness: A contrast to test efficiency, test effectiveness measures and evaluates the bugs and defect ability as well as the quality of a test set. It finds defects and isolates them from the software product and its deliverables. Moreover, the test effectiveness metrics offer the percentage of the difference between the total number of defects found by the software testing and the number of defects found in the software. This is mainly calculated with the assistance of the following formula:

Test Effectiveness (TEF) = (Total number of defects injected + Total number of defects found / Total number of defect escaped) x 100

Test Economic Metrics: While testing the software product, various components contribute to the cost of testing, like people involved, resources, tools, and infrastructure. Hence, it is vital for the team to evaluate the estimated amount of testing, with the actual expenditure of money during the process of testing. This is achieved by evaluating the following aspects:

Total allocated the cost of testing.

The actual cost of testing.

Variance from the estimated budget.

Variance from the schedule.

Cost per bug fix.

The cost of not testing.

Test Team Metrics: Finally, the test team metrics are defined by the team. This metric is used to understand if the work allocated to various test team members is distributed uniformly and to verify if any team member requires more information or clarification about the test process or the project. This metric is immensely helpful as it promotes knowledge transfer among team members and allows them to share necessary details regarding the project, without pointing or blaming an individual for certain irregularities and defects. Represented in the form of graphs and charts, this is fulfilled with the assistance of the following aspects:

Returned defects are distributed team member vise, along with other important details, like defects reported, accepted, and rejected.

The open defects are distributed to retest per test team member.

Test case allocated to each test team member.

The number of test cases executed by each test team member.

Software Testing Key Performance Indicators(KPIs):

A type of performance measurement, Key Performance Indicators or KPIs, are used by organizations as well as testers to get data that can be measured. KPIs are the detailed specifications that are measured and analyzed by the software testing team to ensure the compliance of the process with the objectives of the business. Moreover, they help the team take any necessary steps, in case the performance of the product does not meet the defined objectives.

In short, Key performance indicators are the important metrics that are calculated by the software testing teams to ensure the project is moving in the right direction and is achieving the target effectively, which was defined during the planning, strategic, and/or budget sessions. The various important KPIs for software testers are:

Active Defects: A simple yet important KPI, active defects help identify the status of a defect- new, open, or fixed -and allows the team to take the necessary steps to rectify it. These are measured based on the threshold set by the team and are tagged for immediate action if they are above the threshold.

Automated Tests: While monitoring and analyzing the key performance indicators, it is important for the test manager to identify the automated tests. Through tricky, it allows the team to track the number of automated tests, which can help catch/detect the critical and high priority defects introduced in the software delivery stream.

Covered Requirements: With the assistance of this key performance indicator the team can track the percentage of requirements covered by at least one test. The test manager monitors the these this KPI every day to ensure 100% test and requirements coverage.

Authored Tests: Another important key performance indicator, authored tests are analyzed by the test manager, as it helps them analyze the test design activity of their business analysts and testing engineers.

Passed Tests: The percentage of passed tests is evaluated/measured by the team by monitoring the execution of every last configuration within a test. This helps the team in understanding how effective the test configurations are in detecting and trapping the defects during the process of testing.

Test Instances Executed: This key performance indicator is related to the velocity of the test execution plan and is used by the team to highlight the percentage of the total instances available in a test set. However, this KPI does not offer an insight into the quality of the build.

Test Executed: Once the test instances are determined the team moves ahead and monitors the different types of test execution, such as manual, automates, etc. Just like test instances executed, this is also a velocity KPI.

Defects Fixed Per Day: By evaluating this KPI the test manager is able to keep a track of the number of defects fixed on a daily basis as well as the efforts invested by the team to rectify these defects and issues. Moreover, it allows them to see the progress of the project as well as the testing activities.

Direct Coverage: This KPI helps to perform a manual or automated coverage of a feature or component and ensures that all features and their functions are completely and thoroughly tested. If a component is not tested during a particular sprint, it will be considered incomplete and will not be moved until it is tested.

Percentage of Critical & Escaped Defects: The percentage of critical and escaped defects is an important KPI that needs the attention of software testers. It ensures that the team and their testing efforts are focused on rectifying the critical issues and defects in the product, which in turn helps them ensure the quality of the entire testing process as well as the product.

Time to Test: The focus of this key performance indicator is to help the software testing team measure the time that a feature takes to move from the stage of “testing” to “done”. It offers assistance in calculating the effectiveness as well as the efficiency of the testers and understanding the complexity of the feature under test.

Defect Resolution Time: Defect resolution time is used to measure the time it takes for the team to find the bugs in the software and to verify and validate the fix. Apart from this, it also keeps a track of the resolution time, while measuring and qualifying the tester’s responsibility and ownership for their bugs. In short, from tracking the bugs and making sure the bugs are fixed the way they were supposed to, to closing out the issue in a reasonable time, this KPI ensures it all.

Successful Sprint Count Ratio: Though a software testing metric, this is also used by software testers as a KPI, once all the successful sprint statistics are collected. It helps them calculate the percentage of successful sprints, with the assistance of the following formula:

Quality Ratio: Based on the passed or failed rates of all the tests executed by the software testers, the quality ratio, is used as both a software testing metrics as well as a KPI. The formula used for this is:

Test Case Quality: A software testing metric and a KPI, test case quality, helps evaluate and score the written test cases according to the defined criteria. It ensures that all the test cases are examined either by producing quality test case scenarios or with the assistance of sampling. Moreover, to ensure the quality of the test cases, certain factors should be considered by the team, such as:

They should be written for finding faults and defects.

Test & requirements coverage should be fully established.

The areas affected by the defects should be identified and mentioned clearly.

Test data should be provided accurately and should cover all the possible situations.

It should also cover success and failure scenarios.

Expected results should be written in a correct and clear format.

Defect Resolution Success Ratio: By calculating this KPI, the team of software testers can find out the number of defects resolved and reopened. If none of the defects are reopened then 100% success is achieved in terms of resolution. Defect resolution success ratio is evaluated with the assistance of the following formula:

Process Adherence & Improvement: This KPI can be used for the software testing team to reward them and their efforts if they come up with any ideas or solutions that simplify the process of testing and make it agile as well as more accurate.

Conclusion:

Software testing metrics and key performance indicators are improving the process of software testing exceptionally. From ensuring the accuracy of the numerous tests performed by the testers to validate the quality of the product, these play a crucial role in the software development lifecycle. Hence, by implementing and executing these software testing metrics and performance indicators you can increase the effectiveness as well as the accuracy of your testing efforts and get exceptional quality.

Let us now begin our today’s discussion on how to perform usability testing for your website and discuss various methods to do so.

When you visit a website, like Amazon, eBay, etc., what is the one thing that makes you stay there? Is it the design, offers, or the fact that you can use it easily and find relevant information or product effortlessly? Though all these factors are crucial for retaining a visitor, it is the ease of usability and satisfied user experience that guarantees your happiness and encourages you to stay on a website longer.

So, what is this usability and why is it so critical for your websites?

Nowadays, when the number of the competitors is increasing rapidly, design and content are not enough to retain users, it also requires engaging, intuitive, and responsive user experience, which should be considered by the designers and development teams during the development phase.

Usability, which is defined by ISO as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in the specified context of use” is, therefore, an integral part of a website and is ensured with the assistance of usability testing.

The question then arises:

What is usability testing and how it helps ensure the usability of a website?

Asking people to review your work, might be a time-consuming task, but it always works in your favor. This process can be applied to any discipline, especially to improve the user experience.

Usability testing is one such method of user research or review, which is used to validate the design decisions for an interface as well as to verify its quality, accessibility, and usability by testing it with representative users. It helps create a website/product that connects with users and establishes credibility, builds trust, and ensures customer satisfaction.

Usually conducted by the UX Designer or user researcher during each iteration of the product, it enables them to uncover various issues with the website’s user experience and resolve them to ensure it is usable enough.

Hence, usability testing ensures that the interface of a website is built in a way that it accurately fits the user’s expectations and requirements. Moreover, it determines whether it is user-friendly and if users will come back to it or not.

Methods used to test your website:

An area of expertise of UX/UI designers and developers, usability testing, is performed with the assistance of various methods, which help the team accumulate necessary details about the website’s usability.

Popular testing methods are:

A/B Testing:

A/B testing or split testing is used for an experimental analysis, wherein two versions are compared by the team to choose the best version of the website or its component and to determine the one that performs best.

Adv:

It uses a qualitative and quantitative analysis that validates the intended goal.

Remote Usability Testing: Another important method of usability or user testing, remote usability testing is used when the user and researcher are in different geographical locations. This test is moderated by an evaluator interacting with the participants using various screen sharing tools.

Adv:

It offers developers more realistic insight than lab research and allows them to conduct more research in the shorter period of time.

Co-discovering Learning:

In co-discovering learning, users are grouped together to test the product, while being observed. Test users talk naturally with one another and are encouraged to define what they are thinking about while performing the allocated task.

Adv:

This helps measure the time taken to complete different tasks as well as the instances where the users asked for assistance, among other things.

Expert Reviews: Expert reviews involve UX experts who review the product for any potential issues or defects, which are evaluated by them with the assistance of the following techniques:

Eye-Tracking: This method of usability testing is used to capture physiological data of users conscious and unconscious experiences of using the website. During this testing, the motion of the eye, its movement and position are tracked, to analyze user interactions and time between clicks.

Adv: It helps to identify the most eye-catching, confusing and ignored features on the website.

Performed when there is a requirement for a large number of opinions, these methods help avoid ambiguity and deliver structured information.

Realistic Scripts & Scenarios: This method of usability testing involves both developers and tester, who work together on a preplanned test scenario and imitate the steps that a user while accessing the website.

Adv: They act as a user and replicate the anticipated steps a user takes, which are then assessed by the developers to improve the website’s usability.

Drawing on Paper: Drawing on paper is a popular & cost effective method of usability testing used by designers and developers, wherein they create a website prototypes on a paper and let users test it and its various components, like controls, bars, sliders, etc.

Adv:

This is an effective testing technique as it allows the developers to gain relevant feedback on the paper prototypes easily.

Think Aloud Protocol: Also known as lab usability testing, think aloud protocol, is a qualitative data collection technique, used to understand the user’s own reasons for their website usability behavior. During this process, test sessions are either audio or video recorded for developers future reference.

Whether a website or an app, these methods of usability evaluators can be used by the team to get real users data, which can be utilized to make the product suitable for the target audience.

Now, let’s move on to understanding the process of usability testing.

Process of Website Usability Testing:

The process of usability testing is a simple one and can be executed either by the developers, testers or appointed users. It follows a set of five steps which are:

Planning: The test begins with the team identifying the goals and defining the scope of testing. Furthermore, they agree on the metrics, determine the cost of the usability study and create the test plan and test strategy.

Recruiting: Once the necessary plan is prepared, the team and the resources are assembled and the tasks are assigned accordingly. Finally, the team lead or manager decides the reporting tools and templates, which will be used for test execution.

Test Execution: It is in this stage of the process that the team performs the usability test, during which they communicate the scope of testing and capture unbiased results.

Analysis: After test execution, the team categorizes the results and identifies the patterns among them, which are then used to generate inferences.

Reporting: Finally, once the analysis of the results is completed, the team offers actionable recommendations as well as stakeholder briefing, to help rectify issues and to remove any issues regarding the testing.

Advantages Offered by Usability Testing:

By investing in usability testing, you will not only make your users and potential clients happy but also reap various other benefits, which might help you increase your ROI and create a renowned reputation in the market.

We’re not through yet:

You will also enjoy various other benefits, like:

Improve Retention Rate: Retaining customers is an important source of income for the organization in the retail world. By conducting usability testing organizations can improve the retention rate, as it allows them to understand why users are leaving their site and take necessary preventive measures.

Reduced Costs: It is comparatively cheaper to conduct usability testing, rather than creating a new website or redesigning a one that does not meet the requirements of the user and offers them an unsatisfactory user experience.

Understand User Behavior: From determining the most engaging elements on the website to identifying the pattern of user behavior, usability testing helps the team immensely and offers them data which can be used to create a better website.

Detect Bugs & Defects: Usability testing is immensely helpful in detecting defects and bugs that were not visible to the developers.

Reduce Support Calls: By conducting usability testing, the team can minimize the number of support calls or inquiries users will have to make to the help desk, as they’ll come across fewer usability problems and queries.

Conclusion:

So, these are the various ways to perform usability testing for your website.

Now I’d like to turn it over to you:

Which of these methods do you like the most and which one do you find to be the most effective and useful?

Also, if you have any suggestions, let me know in the comments section below.

Digital Transformation – These two words have changed the enterprise as we know it. Given the intense focus on digital, it has become evidently clear that the world will soon be divided into two parts – that of ‘digital leaders’ and of ‘digital laggards’ as per a Harvard Business Report. Unsurprisingly, HBR believes that it is the digital leaders who will outperform the digital laggards. Digital transformation has impacted business models, customer experiences, and operating models. This trend is all about employing digital technologies to business workflows and operations along with customer interactions. The aim is to enhance existing processes and improve the existing modes of interactions and consequently enable new, better, and more relevant products and processes. So pervasive has been the impact of Digital Transformation that it has topped the CIO agenda in 2017 as per a Wall Street Journal report. Having said that, here’s a look at what this widespread adoption of Digital Transformation means for companies like ours who support the organizations that have embarked on this journey.

Web App Development:

The enterprise today has to keep up with an insatiable demand for apps. It is because of the demand for enterprise-grade, secure, robust, and intuitive applications that organizations developing these apps have had to rethink how applications are created. Development methodologies such as Agile, DevOps, Behaviour Driven Development, and Test Driven Development thus have emerged as key enablers of digital transformation. They give organizations the capability to deliver reliable applications faster. Low-code, rapid application development platforms, also, have been thrust into the spotlight to fuel this digital economy that depends on applications. Given that organizations have to be more consumer-focused in this digital age also means an increased focus on UX. Organizations also have to realize that apps now have to be tightly integrated with existing systems and deliver value to the business. The need for IT agility also means that apps become more customized, simple and modular, and highly secure. App development needs to accommodate these needs. As digital transformation becomes stronger, app development also has to factor in the interfaces with and the working of all networking elements, servers, and databases. Insights into how they are likely to perform under application conditions will become key inputs to delivering service assurance. That is our challenge now.

Mobile App Development:

The mobile has a decisive role in digital transformation. The growing mobile obsession irrespective of geographical, cultural, and social diversity means that enterprises have to calibrate their digital transformation initiatives around mobile consumerism. For software partners like is, this means mobile app development has to look at emerging technologies such as bot frameworks, machine learning, AI etc. to elevate mobile apps to match consumer expectations and have a transformational business impact. Having a mobile plan for all the disparate systems, and ensuring all legacy applications have a mobile front-end will be imperative. Mobile app developers also have to take into consideration business intelligence and analytics as more enterprises move towards SaaS applications and the cloud. At the same time, traditional mobile apps will make way for intelligent mobile apps that employ cognitive API’s and focus on delivering hyper-personalized UX’s to finely-tuned mobile app experiences. With greater digital proliferation, mobile app development will also move towards amalgamating experiences of the web with the mobile to develop apps that are extendable, performance oriented, highly secure, discoverable, and shareable.

Software Testing:

The shift towards methodologies such as Agile and DevOps is changing the way software is tested. The need for fool-proof, secure, available, comprehensive, and robust applications has never been greater than today. Owing to this, shift left testing is becoming popular. Here testing is integrated into the development process itself and starts early in the development cycle. Testing in the digital world is not only about finding faults but also about assisting in creating an application that focuses on customer experience. Testing teams have to now not only look at the business aspect but also focus on providing intelligence for business creation. The speed of testing has to increase and thus, we have to implement higher levels of test automation and leverage technologies such as AI and Machine Learning to make testing smarter. Software testing teams also have to focus on ensuring consistent application performance across different platforms, mobile devices, and operating systems, even with an increased focus on UX. Most importantly, test automation initiatives have to be open to evolution in keeping with constantly evolving application demands.

Cloud

The cloud is a key enabler of digital transformation efforts as it offers enterprises the ease, speed, and scale that businesses need. The digital economy demands application availability. There is no place for latency in this business environment. The cloud emerges as the enabler of efficiencies here to ensure the anytime, anywhere availability of applications and information access. The need for greater computing power, storage, and a robust IT infrastructure can be addressed with the cloud. We have to consider that the cloud will become even more pervasive in enterprises looking at the digital transformation. This is inevitable as it provides enterprises with the capability to continuously innovate, build, test, implement, and experiment with different applications on multiple platforms. Additionally, since digital transformation demands the adoption of a culture of collaboration, it enables people to work more efficiently, to find ways to service customers better, generate revenue, and to find solutions to unsolvable problems. The cloud emerges as its critical enabler of innovation, creativity, and productivity and it has to form a key part of our arsenal.

The true value of digital transformation lies in complete transformation- not just tweaks. This transformation implies disruption and halting a previous trajectory to allow a fundamental change of path. It is only then that you can achieve the goal of digital transformation – to raise the bar and change the ground rules so that you can win in this competitive global economy. And yes, it will be software service partners that will help power that transformation.

Mutation testing is one of the newly developed approaches to test a software application by deriving and using the better quality of test cases. The purpose of mutation testing is to evaluate the effectiveness of the test cases to detect errors in the event of modification or changes in the program code. However, these changes are very small so that it does not affect the overall quality of the application program.

The changes introduced or injected in the program code are generally referred as ‘mutants’. These mutants are injected in the lines of code to replace some variables or operands or syntax or conditions or expressions or statements in order to introduce faults in the code.

Let see a simple example to understand the concept of mutant injection in the program code:

Original Program:

1-Read annual salary.

2-If annual salary > Rs.2.50 Lacs.

3-Income Tax = 10% of 2.50 lacs.

4- Endif.

Above given are the lines of code which is very easy to understand, thus not explaining them. Now, in the above-given program, we try to inject mutant. Let’s see some of them

Mutant Program-1:

1-Read annual salary.

2-If annual salary< Rs.2.50 Lacs.

3-Income Tax = 10% of Rs.2.50 Lacs.

4-Endif.

The original program has been changed to the mutant program by replacing the operator ’>’ with the mutant ‘<’. Further, more unique mutants can be injected to create more mutant programs. Let’s see how

Mutant Program-2:

1-Read annual salary.

2-If annual salary && Rs.2.50 Lacs.

3-Income Tax = 10% of Rs.2.50 Lacs.

4-Endif.

Note:-Invalid operator(&&) injected.

Mutant Program-3:

1-Read annual salary.

2-If annual salary > Rs.2.50 Lacs.

3-

4-End If.

Note:-Deleting the line of code/statement.

Mutant Program-4:

1-Read annual salary.

2-If annual salary > 2.75 lacs.

3-Income Tax =10% of Rs. 2.50 lacs.

4-Endif.

Note:-Changing the value in the statement/line of code.

Now, How to do mutation testing?

We have one original program and its four mutant programs. Test cases with relevant sets of test data are executed over the original and mutant program.

If the results of these test cases are same, then it may be inferred that the test cases are well enough to detect the difference between original and mutant program, and thereby killing the mutant.

And if the results are not same, then it may be concluded that test cases lack to distinguish between original and mutant program, and mutant is still alive. Thus, test cases need to be improved to kill mutants.

Consider following test data for executing test cases over the original program and mutant program-4:

2.80.

2.60.

On feeding 2.80, both original and mutant program generate similar results, thus mutant is killed by the test case. However, with the test data value of 2.60, results will be same, thus mutant is alive and is not detected by the test case, thereby needs improvement in the test cases.

Similarly, executing the above-given test data over the original program and mutant program-2, which generates similar results under both test data values i.e. failure. This means that the test cases are quite effective to detect changes and kill mutants.

The above-stated process needs to be repeated for each different mutant program and for each different set of test data to evaluate and improve the effectiveness and quality of the test cases.

Conclusion:

Although mutation testing is a time-consuming process but is effective to detect loopholes and flaws in the programming code. However, instead of seeing it as a testing technique, mutation testing may be more seen as a test improvement methodology, which improves the effectiveness and quality of the test cases, which ensures good test coverage and subsequently the better test results.

As the world becomes increasingly software-defined and all products become software products, the focus shifts to not only developing newer, better products but to develop them faster. Along with faster development, there has been a shift in the way quality is perceived today. Can we even imagine using a product that is slow or prone to bugs today? In a software-defined world, quality includes reliability and an assurance of uncompromising security. Software development too has undergone a quantum leap over the last few years. Developers are now the superheroes of this software dominated world developing products using new technologies to make our lives simpler and agiler. Developers don’t just create code but are deeply invested in creating products that generate value in our lives. Given this tectonic shift in the manner in which products are developed one big question that may crop up is, “Are great software products created due to great developers or great testers?”

First, a caveat. Clearly, product development calls for a bunch of collaborative efforts. Just as vital as development and testing are defining the user’s needs and adoption behavior, designing a great user experience, and obviously impactful marketing and sales. For the purposes of this blog though we will focus on the nuts and bolts of building the product.

To begin that conversation, we have to take a look at the change that has come about in the software development landscape. The need for great software products to be delivered in the shortest timeframe possible has led to the adoption of development methodologies such as Agile and DevOps. These methodologies are all about faster processes, the use of the latest and the most relevant technology options, and a clear alignment with business demands. As software eats the world, businesses have to release software products faster to meet the ever-changing and increasing consumer demands. The success of an organization has become directly proportional to its capability to release, update, and improve its software. Development teams have thus had to become focused on perfecting releases. Key is making incremental changes to the software as the need arises.

The connection of the end-user with the quality of code is also becoming ever-tighter as the consumer base becomes more used to great digital experiences. Developers are now expected to create intelligent apps that include the latest technologies such as virtual personal assistants (VPAs) etc. New technologies have the potential to transform workplaces and make everyday tasks simpler. Clearly, the developers of today have to know exactly what their audience needs from them and how the application is expected to fulfill a business demand. At the same time, they have to create code that rocks the user’s world. Software products are becoming easier to use but harder to build! Developers now have to focus on creating code that has interconnected parts which render themselves to iterations with ease. Without a doubt, developers have to constantly keep an eye out for the latest technological and business trends and remain updated to create stellar products that can survive in today’s intensely competitive marketplace.

While the role of the developer has risen to one of paramount importance and software delivery reaches Formula 1 speed, the role of the tester has evolved as well. In order to finish first in the race for quality software delivery, the focus on software testing has moved from a good-to-have to a must-have. Software testing can no longer remain an end-of-development exercise. As DevOps and Continuous Delivery move from being a competitive advantage to just par for the course, testing becomes more integrated into the development process itself. Can we imagine fast deployment without adequate testing? Can we release quality software products, releases, or updates fast if the speed of testing does not meet the speed of development? Can we, any longer, afford to leave software testing to the end of the development lifecycle?

While developers have been the key people to recast our society with software, it is the testers who decide the strength of the software in production. It is the testing teams that will identify numerous and creative ways to dispassionately break down a software product so that the product, in the hands of the end-user, behaves as it should. Testing teams are utilizing test automation and technologies such as AI to make the testing process smoother, more expansive, and yet faster, to make sure that broken code does not impede product performance or render the product to vulnerabilities. Testers are the superstars who will dare to raise the uncomfortable questions that ultimately elevate the barometer of quality.

If we look at these two roles closely, we can identify that both developers and testers are working with the same intent – that of creating quality products. However, with new development methodologies such as DevOps coming into play, these two roles are becoming inextricably entwined. Development and testing can no longer function in isolation. If you need a great development team, you need an equally strong testing and test automation team to make sure that the final product is accepted in the market.

The way the world is heading, it is clear that great products can only be created when you not only have great developers but great testers as well. Developers and testers thus both become superheroes fighting the quality war in the software universe…one the Guardians of the Galaxy, and the other the Avengers. Despite their differences, they remain superheroes in their own right, and the biggest battles are won only when they fight on the same side!

While the cost and effort that goes into software testing are high, what is enormously higher is the cost of a software failure. Did you know? Tricentis’ 2018 Software Fail Watch projected that a whopping 3.6 billion people were affected by software failures last year, resulting in economic losses of over $1.7 trillion and a downtime of 268 years! There is no way you can take software testing lightly – for the end result is not just a financial loss but more significantly, a loss of name and customer trust.

The Buzz around Fuzz Testing

Among the myriad types of software testing being undertaken by developers throughout the software development life cycle, fuzzing or fuzz testing has picked up steam of late. An automated software testing technique, fuzz testing involves inputting invalid, unexpected, or random data to a software and monitoring it for crashes, memory leaks, or failing assertions. By demonstrating the presence of bugs rather than their absence, fuzz testing exposes hidden vulnerabilities in a software.

Using fuzz testing to discover coding errors and security gaps in software involves feeding enormous amounts of random data to software in an attempt to make it crash. However, running a fuzzer – which can either be a file or a protocol – even for several weeks, and not finding a bug does not certify the software bug-free; after all, the software may still fail for an input that has not been executed, yet.

Here’s how you fussy you should be with fuzz testing:

Automate values: Since fuzz testing relies on the assumption that there are bugs in every software waiting to be discovered, using an automated program that feeds random inputs into the software is the most appropriate way to spot them. Rather than attempting to list down values that are likely to provoke a crash – which might take you ages – an automated fuzz test inputs a large number of illogical values that a normal programmer would never think of.

Feed a large number of inputs: In order to spot vulnerabilities, use a fuzzer that produces a large number of inputs in a relatively short time. By using a toolchain that automates otherwise manual and tedious tasks, you can enable automated generation of failure-inducing inputs. For eg., google’s OSS-fuzz project produced around 4 trillion test cases a week.

Conduct dynamic program analysis: Using a static program analysis for fuzz testing might just analyze a software without executing it and report problems that actually don’t exist. Hence it is advisable to carry out fuzz testing in combination with dynamic program analysis so as to generate an input that truly witnesses the stated issue.

Automate bug triage: To effectively expose bugs and fix or patch the security critical ones with higher priority, you can group a large number of failure-inducing inputs based on their root cause and then prioritize each individual bug based on severity.

Use sanitizers: To make a fuzzer more sensitive to failures, inject assertions that crash the program when a failure is detected. You can choose from different types such sanitizers to spot different kinds of bugs: deadlocks, undefined behavior, memory leaks, control-flow, etc.

Employ test case reduction: In order to isolate that part of the input that is inducing the failure, a test case reduction (or automated input minimization) tool would eliminate as many input values as possible while still reproducing the original bug. This is especially helpful in cases where the failure-inducing input is large and hence proving difficult for testers to understand the root cause of the bug.

Avoid random plugging: If your fuzz testing program reveals several bugs in your software, plugging them as they appear may not be the right way as random plugging might reduce the robustness of the software. Consider fundamentally hardening the file format through the use of checksums, XML, and grammar-based file formats.

Verify everything: The most common mistake many testers make is to assume that because an instance of the software outputted the correct value once, it will output the same correct value every time. But that can be risky: the instance could have been overwritten, it could have been corrupted, it could have been modified by another program that had a bug or it could even have been intentionally altered in an effort to enhance the software’s security. Hence, assume nothing. Verify everything.

Avoid Failures:

In a world where organizations are losing millions due to software fails (UK-based Provident Financial lost $2.4 billion worth of market value), fuzz testing greatly improves the confidence that an application is robust and secure against unexpected input. Although fuzz testing is simple, it often reveals serious bugs, defects, and potential avenues of attack that must be fixed before the software is shipped. By automatically injecting random permutations of data into a target software until one of those permutations exposes a weakness, Fuzz Testing is helping testers discover software faults and rectify them early in the cycle. Just a few bytes out of place can bring the entire software product down. In such a scenario, fuzz testing helps to prevent such disastrous outcomes. And that’s not fuzzy logic.

The role of testing has never been greater than today. As we move towards the age of software-defined-everything, there is no longer any room for error-prone software. Testing teams are becoming more embedded in the development process. Test automation has been embraced with open arms and has emerged as the critical driver of superior testing and increased testing proficiency. Could we even imagine increasing test coverage to the last possible component had it been done only manually? Could the speed of testing ever match the speed of development had test automation not been there? Clearly, the need for automation stems from the need to test more, test often, test fast and to ensure maximum code coverage.

The Unit Testing Conundrum:

Unit tests may be considered the little brats of the testing world… many in number and often difficult to manage. However, ignore these and you know that your world will soon come crashing down. Unit testing involves the testing of the smallest testable parts of an application. These components or units have to be tested independently and individually to ensure that they are operating as planned. While unit testing can be done manually, it becomes time and resource intensive and because of their exhaustive nature, can be prone to errors when not automated. These tests flow as a part of the Test Driven Development methodology which needs the developers to write failing unit tests to take into account all possible errors, inputs, and outputs. The developers then write code to test the application till the tests pass.

The problem with Unit Testing

As we lean towards methodologies such as Agile, DevOps, and TDD it becomes abundantly clear that unit testing has the power to improve project outcomes. Bugs found early in the unit testing phase are much cheaper to fix than those found later. Making testing a part of the development process ensures the success of agile and DevOps and consequently ensure high-quality and bug-free software development. However, despite these obvious benefits, the struggle with unit testing continues. Some of the reasons for this are:

Time-consuming and tedious test creation process – Creating a comprehensive test suite for unit tests is seen as the least attractive part of the testing process owing to the sheer number of tests that need to be written.

Expensive test maintenance – Any changes to the code mean changes in the associated tests. This adds to the maintenance burden of the unit tests and leads to the idea of ‘double work’ .

Difficulty in isolating tests – Isolating tests also means mocking all those independent dependencies. You get the drift of where we are heading here.

While test automation helps considerably in managing these issues associated with unit tests, it clearly has some issues that still need resolution. The solution to these issues may be found in Guided Test Creation, a unit testing approach that gives testers and developers the assistance they need for test creation and maintenance. All this while working within the developer’s IDE and while leveraging existing tests and mocking frameworks.

Guided Unit Test Creation – the finer details

Most of the developer IDEs do not provide the ‘content’ the developers need to complete the test creation process. The assertions needed to define a test have to be done manually. Mocking frameworks also requires manual coding and this becomes very resource intensive and time-consuming. Guided test creation simplifies this entire process and provides the real-time, context-aware assistance in the developer’s IDE. With the help of these guided unit tests, it becomes far simpler to add the missing content from the unit test skeleton. This helps in writing, conducting, and completing comprehensive unit tests faster.

The aim of the guided unit test can be summed up as follows:

Create test frameworks, configure mock objects, and methods as well as instantiate objects.

Identify method calls that need to be mocked to better isolate test code.

Identify system resources that are created but not released after test completion that could create an unstable test environment.

Detect dependencies and automatically fill the details needed to create a mock framework thereby reducing time and resources needed to create the same.

Provide recommendations that detect the change in code and update the assertions to reflect the new business logic which makes test suite maintenance easier.

Conclusion:

It’s apparent that with the help of Guided Unit Test Creation, developers can resolve many of the problems associated with those pesky unit tests. Several mundane aspects of unit tests are suitably resolved with guided unit test creation. They eliminate the roadblocks that stem from the effort needed to create and maintain these unit tests too. A word of caution, however, while using a guided unit test creation tool, close attention must be paid to ensure that these tools integrate seamlessly with the existing unit testing tools and frameworks. Not doing so would eliminate the time and cost benefit. That addressed, Guided Unit Test creation could be what unit testing has been missing all along.

New, cutting-edge technologies are entering the marketplace at an unprecedented speed. The headline-maker on the block happens to be Blockchain, a technology that grabbed eyeballs as one of Bitcoin’s core components. Today, Blockchain is more than just Bitcoin. The blockchain is emerging as the next tech disruptor. According to a survey by the World Economic Forum, 10% of the global GDP will be relying on Blockchain-based technology by 2027.

So, what is Blockchain Technology?

The brainchild of a person or a group of people using the pseudonym Satoshi Nakamoto, Blockchain is, according to Don & Alex Tapscott, authors Blockchain Revolution (2016), an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value”. The blockchain is essentially a distributed ledger, a continuously growing record list called blocks that are linked and secured using cryptography. This distributed ledger or Blockchain consists of an encrypted digital filing system that creates tamper-proof records in real-time. In other words, Blockchain can be looked upon as an open infrastructure capable of storing several different kinds of assets.

Blockchain and Testing

The blockchain is unique because it removes the need for a middleman to physically oversee transparent actions in real time while at the same time preventing fraud. Supply chain, healthcare, energy, event-ticketing, sales etc. are industries that present themselves to this technology very well. The algorithms used in Blockchain are well-established. Given that it is a distributed system, Blockchain blocks do not have a master copy and are stored in different locations. So, when it comes to testing in Blockchain, given that the algorithms used are sound do we really need to test? A block, when added to a Blockchain, remains there forever. Any changes to one block will render the following blocks invalid. So, a single change in the Blockchain means that all the subsequent blocks have to be changed simultaneously and right away. Since this cannot be done at a later date, testing of the Blockchain becomes quite complex.

Testing Blockchain-based applications are challenging also because there is a significant change in the technology itself. For sure, Blockchain applications will demand the standard testing and validations such as functional testing, performance testing, integration testing, and security testing. But, that apart, testing teams will also need some specialized testing capabilities.

Standard functional and Non-functional testing

Functional Testing: Blockchain technology is finding new applications faster than before. Functional testing of the basic components, the system, and its workings is essential. Testing here is conducted to assess the effectiveness of use-case scenarios and the specific business processes involved.

Integration Testing: Integration testing is important for Blockchain since deployment could be across several systems and environments. Given this, it becomes essential to ensure that the interfaces between the components, the integrations, and the different parts of the system are functioning cohesively. This is essential to ensure performance consistency.

Security Testing:Security testing has to be aggressive for Blockchain applications. The aim is to identify if the application is vulnerable to attacks, assess if the authorization systems are robust, identify if the system protects the data and has the capability to ward off malicious attacks etc. Along with this, it is imperative to test integrity, authentication, confidentiality, and non- repudiation during security testing.

Performance Testing:Blockchain applications are built for speed. This makes performance testing even more important. The performance of an application and the latency vary with networks as well as transaction size. Performance testing in Blockchain includes identifying performance bottlenecks, defining the metrics for tuning the system, and assessing if the application is ready for production.

Specialized Testing

Smart Contract Testing:Smart Contract testing is a specialized testing. Smart contracts lie at the core of the Blockchain validation process. Testing of smart contracts calls for simulating all possible expected and unexpected conditions for all possible contract. Testing looks at business logic combinations and appropriate execution of all the transactions in the context of a dynamically changing and expanding the network.

Peer/node Testing:The power of the Blockchain lies in the shared ledger being exactly the same at each and every node with the same set of and sequence of transactions. This makes it essential to achieve a consensus across all nodes on the order in which the transactions are added to the network. Peer/Node testing for the consistency of transactions is needed. This calls for the testing of the consensus protocol to determine that all the transactions get stored in the proper sequence. This would have to be the case under normal conditions and also under conditions when nodes fail simultaneously or when they do not participate in the network for some time. These tests help ensure that the nodes in the network sync with other validating peers and the integrity of the network and shared ledger are maintained throughout.

Along with all this, testing for block size, chain size, transmission of data, and testing of cryptographical data are also essential to Blockchain applications. Given the sheer number of nodes and the various combinations and transactions that need to be validated, test automation may well prove critical to the success of Blockchain applications.

Conclusion:

The blockchain is an emerging technology, but one that has made everyone sit up and take notice. And like any new technology, how well and how comprehensively we can test will play a key role in its success and adoption and in how much we are able to participate in that success. What does your Blockchain testing strategy look like?

Any roles involved in a project that do not directly contribute toward the goal of putting valuable software in the hands of users as quickly as possible should be carefully considered.” – Stein Inge Morisbak

Does anyone remember the days when the Waterfall model was still around and widely adopted by the enterprises? Over the years most developers have stories of how they realized that it wasn’t giving the best results, that it was slow and inflexible as it followed a sequential process. Fast forward a few years and the principles of Kanban and scrum methodology organically evolved and gave rise to the Agile approach to software development –and we were all on board in a flash. Suddenly, software development teams were able to shift from longer development cycles to shorter sprints, fast releases, and multiple iterations.

But the evolution was not over, as we now know. As Agile shone a spotlight on releasing fast and often, enterprises started loving the opportunity to be more flexible and to speedily incorporate the feedback of their customers. However, this also revealed some drawbacks with the Agile approach. Though the development cycle was faster, there was a lack of collaboration between the developers and the operations team and this was adversely impacting the release and the customer experience.

This gave rise to the new methodology of DevOps which focused on better communication among development, testing, business, and the operations team to provide faster and more efficient development.

So now software development organizations face a choice –should they be Agile? Or do DevOps? Or perhaps somehow both? Let’s look at both approaches more closely, starting with filling in the essential backstory.

The Agile Approach Explained

Software Development approaches like the Waterfall model took several months for completion, where the customers would not be able to see the product until the end of the development cycle. On the other hand, the Agile approach is broken down into sprints or iterations which are shorter in duration during which certain predetermined features can be developed and delivered. There are multiple iterations and after every iteration, the software team can deliver a working product. The features and enhancements are planned and delivered for every succeeding iteration after discussions (negotiations?) between the business and the development teams. In other words, Agile is focused on iterative development, where the requirements and solutions are developed because of collaboration between cross-functional and self-organizing software teams.

What is DevOps?

This is the age of Cloud and SaaS products. That being the case, DevOps can be defined as a set of practices enabling automation of processes between the software development and the IT teams for building, testing, and deploying the software in a faster and more efficient manner. DevOps is based on cross-functional collaboration and involves automation and monitoring right from the integration, testing, releasing, and deployment along with the management of infrastructure.

In short, DevOps helps in improving collaboration and productivity by integrating the developers and the operations team. Typically, DevOps calls for an integrated team comprising developers, system administrators, and testers. Often, Testers turned into DevOps engineers are assigned the end-to-end responsibility of managing the application software. This may involve everything from gathering requirements to development, deployment, and gathering user feedback to implementing the final changes.

How do they compare (or contrast)?

Creating and deployment of software: Agile is purely a software development process. That means, the development of software is an inherent part of the agile methodology. Whereas Devops can deploy software which may have being developed using other methodologies, based on either Agile or non-agile approaches.

Planning and documentation: The Agile method is based on developing new versions and updates during regular sprints (a time frame decided by the team members). Besides, daily informal meetings are key to the Agile approach, where team members are encouraged to share progress, set goals, and ask for assistance if required. To that extent, the emphasis on documentation is less.

On the other hand, DevOps teams may not have daily or regular meetings but plenty of documentation is required for proper communication across the teams for effective deployment of the software.

Scheduling activities and team size: Agile is based on working in short and pre-agreed sprints. Traditionally sprints can last for a week to 1 month or so at the extreme. The team sizes are also relatively smaller as they can work faster with fewer individuals working on the effort. DevOps can comprise of several teams using different models such as Kanban, Waterfall model, or scrum where all of them are required to come together for discussing regarding software deployment. These teams could be larger and are by design much more cross-functional.

Speed and risk: Agile releases, while frequent, are significantly less than what DevOps teams aim for. There are DevOps products out there that release versions with new features multiple times in an HOUR! The application framework and structure in Agile approach needs to be solid to incorporate the rapid changes. As the iterative process involves regular changes to the architecture, it’s necessary to be aware of every change related to the risks to ensure quick and speedy delivery. This is true of DevOps also, but the risk of breaking previous iterations is far greater in DevOps as the releases are much more frequent and follow much faster on the heels of one another than in the Agile approach.

Conclusion

DevOps is a reimagining of the way in which the software needs to be configured and deployed. It adds a new dimension to the sharp end of the value chain of software development i.e the delivery to the customers. There is some talk about that that DevOps will replace Agile, but our view is that DevOps complements Agile by streamlining deployment to enable faster, more effective, and super-efficient delivery to the end users. That’s a worthy goal –so why choose between the two!

Testing is a ripe field for applying AI because testing is fundamentally about inputs and expected outputs…… Testing combines lots of human and machine-generated data. Folks in testing often don’t have much exposure to AI, but that will change quickly, just like everyone else in the world is waking up to the power of AI.” – Jason Arbon, Author, and CEO of test.ai

We could say that automated software testing is essentially a quality control system that vets the operational aspects of a software product. The aim is to create a testing process that is rigorous and that operates through one or multiple test automation frameworks. Typically, upon completion, the tools report the results and compare outcomes with previous testing cycles. This is the age of Big Data and Analytics – it stands to reason that innovators have developed intelligent analytics solutions that offer insights designed to translate these test results into actionable information for future improvement. These solutions proactively identify problem areas in the testing process and indicate the way forward to achieve a high-quality software product. Let’s take a closer look at how analytics can help test automation.

Use of Analytics

In this context, analytics enables software developers to critically evaluate the performance of their test automation. They can track the various metrics and parameters involved in the creation of the test automation and the performance of the automated software testing exercise. Error logs embedded in the dashboard can spotlight the areas of improvement. Similarly, data about the number and the kind of functions that pass muster indicate the health of the software product that is being tested. The final status of the test results presents a perfect picture of the state of functionalities of the tested software. The graphical representations in the analytics dashboard portray a clear picture of testing outcomes that is easy to read and understand for everyone.

Predictive Analytics:

This aspect of analytics uses mathematical algorithms and machine learning technologies to forecast outcomes of software testing procedures. This technique uses current and past data to generate insights and locate potential points of failure in software testing outcomes. This enables the development and testing leaders to proactively address issues early in the lifecycle, and hence faster and easier. The use of predictive analytics also helps to detect delays and issues in software testing cycles. It also helps to monitor team productivity in testing cycles that involve human beings. Software developers can also run risk mitigation efforts when they use predictive analytics in testing procedures.

Benefits of Analytics in Testing:

Analytical reports draw on data that resides in multiple sources. This helps to present a more complete picture in real-time. The insights are clear and present, the actions to be taken are apparent, and the results can be tracked. The granular nature of the feedback generated by analytics should help software designers and testers to correct specific errors and the speed up slow processes.

The application of analytics should help software testing systems to overcome traditional or legacy limitations. The visual depiction of data in test performance and test history charts creates significant grounds to improve the testing procedures of the future. It is true that even today, automated software testing may fail for a variety of reasons, but the judicious application of analytics can increase the utility and the chances of success. In addition, interactive analytics-driven dashboards can offer enhanced monitoring and reporting capabilities for software testers and software developers. Further, analytics helps to expand the productivity of complex software testing tools while boosting the productivity of the testing team. This can help to release higher-quality products faster and more often.

The combination of test automation and advanced analytics will enable software development and testing managers to spend more time on strategic activities that drive greater business value over a longer term.

The Future of Automation in Testing:

Enterprises today are driving a relentless focus on quality. Current and future products are undergoing design changes that will make them even more intuitive and easy to use. The user interfaces will be the most critical aspect and they must be tested for reliable operation at all times. The deployment of analytics should help software developers and designers to better test software and create perfect products for clients. Intelligent observations and business insights derived from analytics will drive better, more targeted actions. Therefore, testing strategies and test plans will be refined and re-engineered to create greater scope for analytics in automation. It’s all set to be the and analytics-driven automated age in software testing – are your plans ready?

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